FlowSpec MCP
A PRD-first multi-agent MCP server that wraps agent definitions, collaboration rules, and staged delivery into a local stdio service for standardized software delivery workflows.
README
FlowSpec MCP
English | 简体中文
Alias: Stay the Course (Keep Moving Forward)
FlowSpec MCP is a PRD-first multi-agent MCP server for standardized software delivery workflows. It wraps the agent definitions, collaboration rules, and staged delivery process from claude-standard-dev-team into a local stdio MCP service that can run across different MCP hosts.
Why FlowSpec
Many multi-agent tools fail not because they lack agents, but because they lack structure. FlowSpec MCP focuses on three ideas:
Spec first: createPRD, contracts, and task lists before executionWorkflow closed-loop: move from planning to delivery through explicit phasesModel-agnostic: work with MCP-compatible hosts instead of tying the workflow to one model vendor
In short:
Write the spec first, then let the agents execute.
Highlights
- Standard
stdio MCP server 1orchestrator plus multiple specialist agents- Supports
plain,minimal-json, andfull-artifact-json - Supports full
Phase 0 -> Phase 11workflow execution - Writes generated artifacts to a target directory
- Includes smoke tests and full integration tests
- Runs without the
claudeCLI
Recommended Workflow
Recommended order of use:
- Generate a standardized
PRD - Generate
TECH_SPEC,API_CONTRACT, andDB_SCHEMA - Let the orchestrator dispatch specialist agents by phase
- Add human checkpoints after
Phase 1andPhase 2 - Use QA, security, review, and acceptance reports as release gates
Model Recommendations
Model quality varies a lot across structured output, code generation, and long-running workflows. For best results, prefer stronger models for end-to-end execution.
Recommended priority:
ClaudeGPTDeepSeek V4 ProMiniMax
Guidance:
- If your host supports
sampling, connect a stronger model and run the full workflow automatically - If your host does not support
sampling, letFlowSpec MCPproduce prompt packages and pass them to your preferred model manually
Architecture
Default workflow roles:
orchestratorfor global coordinationproduct-managerforPRDsoftware-architectfor contracts and technical specsui-designerfor design system outputdatabase-optimizerfor database implementationbackend-architectfor backend implementationfrontend-developerfor frontend implementationtesting-evidence-collectorfor QA evidencesecurity-engineerfor security reviewcode-reviewerfor code reviewreality-checkerfor final acceptancetechnical-writerfor delivery documentation
Available Tools
health_checklist_agentsget_agent_promptget_workflow_summarybuild_execution_planrun_agentrun_orchestratorrun_governed_workflowrun_full_workflow
Output Modes
plain
- Human-readable text
- Best for manual prompt inspection
minimal-json
- Minimal structured JSON
- Best for rule enforcement and lightweight orchestration
full-artifact-json
- Full structured JSON
- Includes complete artifact contents in
artifacts - Best for writing staged deliverables to disk
Requirements
- Node.js
>= 20 - Local access to the
claude-standard-dev-teamsource repository
Source resolution order:
TEAM_SOURCE_PATH../claude-standard-dev-teamif the env var is not set
Install
cd <PATH_TO_FLOWSPEC_MCP>
npm install
Start
cd <PATH_TO_FLOWSPEC_MCP>
npm start
Notes:
- The process stays attached to stdio and waits for an MCP host to connect
- In practice, it is better to let Claude Desktop, Cursor, or a custom MCP client launch it
MCP Configuration
See mcp.config.sample.json for a generic example.
{
"mcpServers": {
"flowspec-mcp": {
"command": "node",
"args": [
"C:\\path\\to\\flowspec-mcp\\server.js"
],
"env": {
"TEAM_SOURCE_PATH": "C:\\path\\to\\claude-standard-dev-team"
}
}
}
}
Claude Desktop
- Config file usually lives at
%APPDATA%\\Claude\\claude_desktop_config.json - See claude_desktop_config.sample.json
Cursor
- Config file usually lives at
%USERPROFILE%\\.cursor\\mcp.json - Uses the same structure as the generic MCP config
Full Workflow
Default full workflow phases:
Phase 0orchestratorPhase 1product-managerPhase 2software-architectPhase 2.5ui-designerPhase 3orchestratorPhase 4database-optimizerPhase 5backend-architectPhase 5 QAtesting-evidence-collectorPhase 6frontend-developerPhase 6 QAtesting-evidence-collectorPhase 7security-engineerPhase 8code-reviewerPhase 9devops-automatorPhase 10reality-checkerPhase 11technical-writer
Examples
Generate only the execution plan
build_execution_plan(userRequest="Build a Todo Lite app", mode="full-workflow")
Run a single agent
run_agent(
agentName="product-manager",
phase="Phase 1",
artifactType="PRD",
projectName="todo-lite",
responseMode="full-artifact-json",
targetDir="C:\\output\\todo-lite",
task="Generate a complete PRD"
)
Run the full workflow
run_full_workflow(
projectName="todo-lite",
userRequest="Build a minimal Todo Lite app with create, list, toggle, docs, code skeleton, and deployment files.",
targetDir="C:\\output\\todo-lite"
)
Testing
npm test
npm run smoke
npm run test:integration
The full integration test:
- starts the local MCP server
- connects a mock sampling host
- verifies tool discovery
- verifies orchestrator and full workflow execution
- writes artifacts into
reports/ - generates test reports
Publishing Notes
- Keep sample configs path-neutral
- Do not commit local generated outputs such as
reports/ - Do not publish machine-specific usernames or absolute paths
- Include
LICENSE,CHANGELOG, release tags, and screenshots for a cleaner project page
Limitations
- This project is a local MCP adaptation of the upstream agent rules, not the official upstream MCP server
- If the host does not support
sampling, execution falls back to prompt packages - The current validation focuses on protocol integration, orchestration, constraints, and staged workflow closure, not on universal model quality guarantees
Roadmap
- Retry logic and persisted workflow state
- Resume from checkpoints
- Stronger artifact validation
- More model gateway integrations
- Regression testing against real project repositories
Source And Thanks
This project is inspired by and built with reference to the upstream project xuanbingbingo/claude-standard-dev-team.
Thanks to the original authors and contributors for publishing the agent definitions, workflow ideas, and engineering conventions that made this local MCP adaptation possible.
Contact
- Maintainer contact:
feng#moonstack.org ('#' to '@')
License
Released under the MIT License.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.